Evidence map›Paper›PMID 40169731›Full record

ArticleScientific reports2025

The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems.

Xiaowen Lv, Ali Basem, Mohammadtaher Hasani, Ping Sun, Jingyu Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiaowen LvCollege of Information Science and Technology, ZheJiang ShuRen University, Hangzhou, 310015, China.
Ali BasemFaculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq.
Mohammadtaher HasaniDepartment of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. hasani.mohammadtaher@gmail.com.
Ping SunCollege of Information Science and Technology, ZheJiang ShuRen University, Hangzhou, 310015, China.
Jingyu ZhangSchool of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the integration of Robust Model Predictive Control (RMPC) and Deep Learning to enhance the performance and adaptability of energy systems, focusing on Combined Heat and Power (CHP), Power-to-Hydrogen, and Power-to-Gas Methane applications. The proposed framework combines RMPC's robustness with Deep Learning's ability to learn and adapt, improving control precision and operational efficiency. Extensive simulations indicate that the integrated RMPC-Deep Learning system improves control accuracy by 8.02% compared to conventional methods, while also reducing energy consumption by 12.14%. These quantitative results demonstrate the effectiveness of the proposed system in addressing challenges such as operator saturation, showcasing its potential to optimize energy systems under dynamic conditions. This work highlights the transformative role of merging RMPC with Deep Learning, providing a robust and adaptable solution for energy management in complex applications.

Indexed as

CHPDeep learningEnergy systems optimizationPower-to-Gas methanePower-to-HydrogenRMPCSaturation

Identifiers

PMID40169731
PMCPMC11961586

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.